A Multi-Strategy Enhanced Wolf Pack Algorithm for Three-Dimensional Path Planning of Unmanned Aerial Vehicles

In this paper, a Multi-strategy Enhanced Wolf Pack Algorithm (MSEWPA) is proposed to address the three-dimensional (3D) path planning problem for unmanned aerial vehicles (UAVs) in complex environments. Initially, a mathematical model for 3D path planning is constructed, comprehensively considering constraints such as UAV operational efficiency, path safety risks, performance limitations, obstacle avoidance requirements, and noise limits in urban functional areas. Subsequently, the design of the MSEWPA algorithm is elaborated in detail, including the utilization of the Good Lattice Point (GLP) theory to optimize population initialization for enhanced global search capability, the integration of selection, crossover, and mutation operations from the Differential Evolution (DE) algorithm to augment the randomness of wandering, the introduction of a behavior transition factor for adaptive behavior adjustment, the incorporation of light propagation phenomena to improve random search capabilities during the running process, and the design of multiple siege strategies to guide the exploration of globally optimal solutions. To validate the robustness of the algorithm, sensitivity analysis is conducted on key parameters to determine their optimal settings, and ablation experiments are performed to verify the effectiveness of each improvement strategy. Experimental results on the CEC-2017 benchmark test functions demonstrate that MSEWPA excels in solving complex optimization problems, achieving rapid convergence to high-quality global optimal solutions. Furthermore, in four path planning problems of varying complexity, MSEWPA outperforms 11 other state-of-the-art metaheuristic optimization algorithms, demonstrating a strong balance between global and local exploration capabilities. This provides an effective solution for UAV 3D path planning.